A Multi-Source Data Fusion-Based Financial Anti-Fraud Model: Collaborative Analysis of Graph Neural Networks and Sequence Models

A Multi-Source Data Fusion-Based Financial Anti-Fraud Model: Collaborative Analysis of Graph Neural Networks and Sequence Models

I. Problem Description
In financial anti-fraud scenarios, fraudulent activities often exhibit two key characteristics: first, cross-account correlations (such as organized fraud), and second, temporal anomaly patterns within individual accounts (such as high-frequency transactions within a short period). Traditional single models can only capture local information, whereas multi-source data fusion models, by combining Graph Neural Networks (GNNs) and sequence models (such as LSTM or Transformer), can simultaneously mine fraud clues from both transaction networks and individual behavioral sequences. This problem requires addressing the following core issues:

  1. How to construct multi-source data that fuses transaction relationship graphs and behavioral sequences?
  2. How can Graph Neural Networks and sequence models be trained collaboratively?
  3. How to address the heterogeneous fusion of outputs from the two types of models?

II. Detailed Solution Process

Step 1: Multi-Source Data Construction and Feature Engineering

  • Graph Structure Data Construction:
    • Nodes: Each user or account serves as a node.
    • Edges: An edge is established if there exists a relationship such as a transaction, shared device, or IP association between two accounts. Edge weights can be dynamically adjusted based on transaction frequency, amount, or association strength.
    • Node Features: Include static attributes (e.g., registration time, occupation) and dynamic statistical features (e.g., mean transaction count over the past 7 days).
  • Sequence Data Construction:
    • For each account, aggregate transaction records by time window (e.g., 1 hour) to generate multi-dimensional temporal features (e.g., transaction amount, transaction type encoding, counterparty account risk label).
    • Sequence lengths need to be standardized (e.g., truncated or padded to a fixed length of 50 records).

Step 2: Dual-Channel Model Design

  • Graph Neural Network Channel (Capturing Spatial Correlations):
    • Use Graph Convolutional Networks (GCN) or Graph Attention Networks (GAT) to aggregate neighbor information. For example, compute node embeddings using a 2-layer GCN:

\[ H^{(l+1)} = \sigma\left(\tilde{D}^{-\frac{1}{2}} \tilde{A} \tilde{D}^{-\frac{1}{2}} H^{(l)} W^{(l)}\right) \]

where $\tilde{A}$ is the adjacency matrix with self-loops, $\tilde{D}$ is the degree matrix, and $H^{(l)}$ is the node embedding at layer l.
  • Output: A graph embedding vector for each node (e.g., 128-dimensional).
  • Sequence Model Channel (Capturing Temporal Patterns):
    • Use Bi-LSTM or Transformer to encode the transaction sequence:
      • Bi-LSTM: Concatenate the final hidden states of the forward and backward LSTMs to form the sequence embedding.
      • Transformer: Utilize the self-attention mechanism to weight important time steps, outputting a sequence-level representation.
    • Output: A sequence embedding vector for each account (e.g., 128-dimensional).

Step 3: Heterogeneous Feature Fusion Strategy

  • Concatenation + Fully Connected Layer:
    • Concatenate the graph embedding vector and the sequence embedding vector into a 256-dimensional fusion vector.
    • Perform dimensionality reduction and nonlinear transformation through a fully connected layer:

\[ y = \sigma\left(W_f [h_{\text{graph}} \| h_{\text{sequence}}] + b_f\right) \]

where $W_f$ is the weight matrix and $\sigma$ is the ReLU activation function.
  • Attention-Weighted Fusion (Advanced Method):
    • Introduce learnable attention weights to dynamically adjust the contribution of the dual channels:

\[ h_{\text{fuse}} = \alpha \cdot h_{\text{graph}} + (1-\alpha) \cdot h_{\text{sequence}}, \quad \alpha = \text{sigmoid}(W_a [h_{\text{graph}} \| h_{\text{sequence}}]) \]

Step 4: Model Training and Optimization

  • Loss Function: Use weighted cross-entropy loss to address the scarcity of fraud samples:

\[ \mathcal{L} = -\frac{1}{N} \sum_{i=1}^N \left[ w \cdot y_i \log(\hat{y}_i) + (1-y_i) \log(1-\hat{y}_i) \right] \]

where the weight \(w = \frac{N_{\text{normal samples}}}{N_{\text{fraud samples}}}\).

  • Training Techniques:
    • Apply batch normalization (BatchNorm) to graph data and sequence data separately to prevent gradient explosion.
    • Employ early stopping to avoid overfitting, retaining the best model on the validation set.

Step 5: Model Interpretability and Application

  • Key Feature Attribution:
    • Graph Channel: Use GNN interpretation tools (e.g., GNNExplainer) to identify high-risk associated edges.
    • Sequence Channel: Utilize Transformer attention weights to locate anomalous time points.
  • Real-Time Inference Optimization:
    • Graph embeddings can be pre-computed and cached, while sequence embeddings require real-time updates to balance efficiency and timeliness.

III. Summary
This model leverages the collaboration between GNNs and sequence models to simultaneously capture the group correlations and individual behavioral anomalies of fraud. The core innovations lie in the structured construction of multi-source data, the attention-based fusion mechanism for dual-channel embeddings, and the optimization for sample imbalance in financial scenarios. In practical applications, attention must be paid to the synergy between data update frequency and model iteration, such as aligning the dynamic update strategy of the transaction graph with the sequence window length.